Using Circuit Theory to Design Connected Landscapes

Principal Investigator(s):

Dispersal is a key process in maintaining healthy and viable animal populations. Maintaining movement and connectivity across landscapes can help to reduce inbreeding, rescue small populations from extinction, and/or colonize new habitat. But with limited funding and constant threats of habitat loss, how do we choose which habitats to protect so that landscapes will stay well-connected for wild animal species? Brad McRae has been working on these questions with some novel twists: he's applying ideas from electrical engineering to the study of connectivity in landscapes.

So what do animals and electrons have in common? Plenty, according to the mathematics behind electronic circuits on the one hand and random walk theory on the other. Commonalities between the theories have been exploited in other disciplines, like the study of neural and social networks, but their use in ecology and evolution is new. Because of these connections, powerful computing tools developed in engineering can be used to analyze connectivity in large and complex landscapes. For example, by simultaneously taking into account multiple dispersal pathways, circuit theory can be used to explain patterns of gene flow and genetic differentiation among animal populations, and to predict how these patterns might change under future land use or climate change scenarios. Or, if current flows across a model landscape, the density of flow in different areas can be used to predict which portions of the landscape are most used by dispersing animals, thus identifying critical connective habitats. The goals are to understand how landscape heterogeneity structures populations, and to efficiently prioritize habitats for conservation so that we can maintain well-connected landscapes into the future.

Graphic: Simulated connectivity among core habitat areas for mountain lionsResearch Collaborators: Brett Dickson and Rick Hopkins, Live Oak Associates, with generous support from Vicki Long